Process reveals structure: How a network is traversed mediates expectations about its architecture
Year: 2017
Type: article
Abstract: Network science has emerged as a powerful tool through which we can study the higher-order architectural properties of the world around us. How human learners exploit this information remains an essential question. Here, we focus on the temporal constraints that govern such a process. Participants viewed a continuous sequence of images generated by three distinct walks on a modular network. Walks varied along two critical dimensions: their predictability and the density with which they sampled from communities of images. Learners exposed to walks that richly sampled from each community exhibited a sharp increase in processing time upon entry into a new community. This effect was eliminated in a highly regular walk that sampled exhaustively from images in short, successive cycles (i.e., that increasingly minimized uncertainty about the nature of upcoming stimuli). These results demonstrate that temporal organization plays an essential role in learners' sensitivity to the network architecture underlying sensory input.
Source: Scientific Reports
Institution: University of Pennsylvania
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funders: National Science Foundation, John D. and Catherine T. MacArthur Foundation, Alfred P. Sloan Foundation, National Institutes of Health, Office of Naval Research, National Institute of Mental Health, National Institute on Deafness and Other Communication Disorders, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Army Research Office, Army Research Laboratory
Awards: G1001159199, G1024130705, G1041393086, Multimodal brain maturation indices modulating psychopathology and neurocognition, G1099660080, G1177916794, G129050353, G129985460, G1350070852, G1483473580, G157307749, G1630141903, G1714448283, G1778482337, G1993459732, G2066955304, G2130352337, G2236980202, G2247336289, G2252567680, G2481140831, G2864901048, G2887601406, G2901702611, G2977132040, G3121740702, G3137615481, G3330175468, G3334283873, G3424441068, G3564833335, G3611952402, G3690479056, G3755125221, G3824545241, G4069802742, G4475921622, G4503518981, G4526399220, G4548301804, G4802959984, G4823423002, G5172716836, G5253651465, G5259331294, G5419322094, G5496178531, G572628577, G5760707814, NCS-FO: Collaborative Research: A Mechanistic Model of Cognitive Control, G5884259427, CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks, G6409978989, G6467593695, G6671297155, G6689184838, Longitudinal multi-modal neuroimaging of irritability in youth, Linguistic and Nonlinguistic Functions of Frontal Cortex, G6863769665, G6981911394, Evolution of the Linked Architecture of Network Control and Executive Function in Adolescence, G7159985717, G7236036932, G7452299184, Conceptual Combination, WORKSHOP: Quantitative Theories of Learning, Memory, and Prediction, G7530591242, G7538535954, G7594130654, G7747382488, G7838535007, G7989609580, G8164210364, G8278114317, G8318881156, G8454816522, CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks, G8554419822, G8796586358, G9000002492, G9000056496, G9000279655, G9000335651, G91481811, G916315028, G91658921
OpenAlex ID: W2949994376